We study access to ride-sharing across geographical regions using both theoretical and empirical analyses. We specifically model and examine the effects of economies of density in ride-sharing. Our model predicts that (i) economies of density skew access to ride-sharing away from less dense regions; (ii) the skew will be more pronounced for smaller platforms (i.e., “thinner markets”); and (iii) ride-sharing platforms do not find this skew efficient and thus, use price and wage levers to mitigate (but not eliminate) it. We show that these insights are robust to whether the source of economies of density is the supply side or the demand side. We then calibrate our model using ride-level Uber data from New York City. We use the model to simulate counterfactual scenarios, offering a quantitative evaluation of our theoretical results and informing platform strategy and policy. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02699 .
Ghili et al. (Wed,) studied this question.